Self-assembly using mechanistic consumer-resource models

Python version

3.7.3 (default, Mar 27 2019, 16:57:26) 
[Clang 4.0.1 (tags/RELEASE_401/final)]

Settings

Import packages

from IPython.display import Image
from community_simulator import *
from community_simulator.usertools import *
from community_simulator.visualization import *
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.backends import backend_pdf as bpdf
#colors = sns.color_palette()
#%matplotlib inline

Functions for simulation

1. prepare_experiment(assumption)
   • Make params and species_pool
2. make_synthetic_*()
   • make_synthetic_mono(assumptions)
   • make_synthetic_community()
   • make_initial_state(assmuptions, N0)
3. simluate_community(plate, assumptions, params_simulation, file_name)

N0 = make_synthetic_community(species_list = [13, 16, 17, 33, 49, 51, 68, 74, 85, 94, 152, 155, 171, 188, 191], assumptions = assumptions, number_species = 2, initial_frequency = [[0.5, 0.5], [0.95, 0.05], [0.05, 0.95]])
init_state = make_initial_state(assumptions, N0)

# Make plate
plate = Community(init_state, dynamics, params, scale = 10**6, parallel = True)

# Simulation
simulate_community(plate, assumptions, params_simulation, file_name = "data/self_assembly-pair-W0", write_composition = True)

source_python("script/04A-self_assembly-01-experiment_functions.py")

Simulation

Parameters

# Assumptions
assumptions = a_default.copy() # Start with default parameters
assumptions.update({'n_wells':96, 'c1' :0.01, 'muc':0.1, 'm':0}) # Switch off mortality

# Prepare experiment
params, species_pool = prepare_experiment(assumptions)

## Simulation parameters
params_simulation = {
    "n_propagation": 12, # Length of propagation, or hours within a growth cycle
    "n_transfer": 10, # Number of transfer, or number of passage
    "dilution": 1/1000, # Dilution factor for transfer
}

96 self-assembled communities

# Make initial state
init_state = MakeInitialState(assumptions)

# Make plate
plate = Community(init_state, dynamics, params, scale = 10**6, parallel = True) 

# Simulation
simulate_community(plate, assumptions, params_simulation, file_name = "data/self_assembly-community", write_composition = True)

Monocultures

# Make initial state
N0 = make_synthetic_mono(assumptions)
init_state = make_initial_state(assumptions, N0)

# Make plate
plate = Community(init_state, dynamics, params, scale = 10**6, parallel = True) 

# Simulation
simulate_community(plate, assumptions, params_simulation, file_name = "data/self_assembly-mono", write_composition = True)

Plot result

source("script/04A-self_assembly-02-read_community_data.R")

if(write_all_csv) {
  fwrite(df_self_assembly, root$find_file("output/report/data/temp/df_self_assembly.txt"))
  fwrite(df_mono, root$find_file("output/report/data/temp/df_mono.txt"))
  fwrite(df_grower, root$find_file("output/report/data/temp/df_grower.txt"))
}

Richness

Relative abundances, 4 self-assembled communities for example

Relative abundances of 96 self-assembled communities at 10th transfer

p_self_assembly[[4]]

pdf(root$find_file("output/report/figure/04A-self_assembly-self_assembled_communities.pdf")); p_self_assembly[[4]]; invisible(dev.off())

Monoculture growth

p_mono[[2]]

pdf(root$find_file("output/report/figure/04A-self_assembly-monoculture_growth.pdf")); p_mono[[2]]; invisible(dev.off())

List of grower identified by monoculture

df_mono %>%
  filter(Type == "consumer", Transfer == 10) %>%
  pull(ID) %>% as.character() %>% paste0(collapse = ", ")

[1] "13, 16, 17, 18, 21, 33, 38, 49, 51, 66, 68, 74, 83, 85, 94, 110, 115, 118, 142, 152, 155, 158, 171, 177, 183, 188, 191"

Pairwise competition

Species list from equilibrial community W0

df_self_assembly %>%
  filter(Type == "consumer", Transfer == 10) %>%
  filter(Community == "W14") %>% 
  pull(ID) %>%
  paste0(collapse = ", ")

Essential functions for community self-assembly

source_python("script/04A-self_assembly-01-experiment_functions.py")

Pairwise competition in self-assembled community W0

# Assumptions
assumptions = a_default.copy() # Start with default parameters
assumptions.update({'n_wells':96, 'c1' :0.01, 'muc':0.1, 'm':0}) # Switch off mortality

# Prepare experiment
params, species_pool = prepare_experiment(assumptions)

## Simulation parameters
params_simulation = {
    "n_propagation": 12, # Length of propagation, or hours within a growth cycle
    "n_transfer": 10, # Number of transfer, or number of passage
    "dilution": 1/1000, # Dilution factor for transfer
}

# Make initial state
N0 = make_synthetic_community(species_list = [13, 16, 17, 33, 49, 51, 68, 74, 85, 94, 152, 155, 171, 188, 191], assumptions = assumptions, number_species = 2, initial_frequency = [[0.5, 0.5], [0.95, 0.05], [0.05, 0.95]])
init_state = make_initial_state(assumptions, N0)

# Make plate
plate = Community(init_state, dynamics, params, scale = 10**6, parallel = True)

# Simulation
simulate_community(plate, assumptions, params_simulation, file_name = "data/self_assembly-pair-W0", write_composition = True)

Pairwise competition in self-assembled community W1

# Assumptions
assumptions = a_default.copy() # Start with default parameters
assumptions.update({'n_wells':96, 'c1' :0.01, 'muc':0.1, 'm':0}) # Switch off mortality

# Prepare experiment
params, species_pool = prepare_experiment(assumptions)

## Simulation parameters
params_simulation = {
    "n_propagation": 12, # Length of propagation, or hours within a growth cycle
    "n_transfer": 10, # Number of transfer, or number of passage
    "dilution": 1/1000, # Dilution factor for transfer
}

# Make initial state
N0 = make_synthetic_community(species_list = [18, 21, 38, 51, 94, 142, 152, 155, 158, 171, 183, 188], assumptions = assumptions, number_species = 2, initial_frequency = [[0.5, 0.5], [0.95, 0.05], [0.05, 0.95]])
init_state = make_initial_state(assumptions, N0)

# Make plate
plate = Community(init_state, dynamics, params, scale = 10**6, parallel = True)

# Simulation
simulate_community(plate, assumptions, params_simulation, file_name = "data/self_assembly-pair-W1", write_composition = True)

Pairwise competition in self-assembled community W2

# Assumptions
assumptions = a_default.copy() # Start with default parameters
assumptions.update({'n_wells':96, 'c1' :0.01, 'muc':0.1, 'm':0}) # Switch off mortality

# Prepare experiment
params, species_pool = prepare_experiment(assumptions)

## Simulation parameters
params_simulation = {
    "n_propagation": 12, # Length of propagation, or hours within a growth cycle
    "n_transfer": 10, # Number of transfer, or number of passage
    "dilution": 1/1000, # Dilution factor for transfer
}

# Make initial state
N0 = make_synthetic_community(species_list = [13, 16, 18, 21, 33, 66, 85, 110, 115, 118, 155, 171, 177, 191], assumptions = assumptions, number_species = 2, initial_frequency = [[0.5, 0.5], [0.95, 0.05], [0.05, 0.95]])
init_state = make_initial_state(assumptions, N0)

# Make plate
plate = Community(init_state, dynamics, params, scale = 10**6, parallel = True)

# Simulation
simulate_community(plate, assumptions, params_simulation, file_name = "data/self_assembly-pair-W2", write_composition = True)

Pairwise competition in self-assembled community W14

# Assumptions
assumptions = a_default.copy() # Start with default parameters
assumptions.update({'n_wells':96, 'c1' :0.01, 'muc':0.1, 'm':0}) # Switch off mortality

# Prepare experiment
params, species_pool = prepare_experiment(assumptions)

## Simulation parameters
params_simulation = {
    "n_propagation": 12, # Length of propagation, or hours within a growth cycle
    "n_transfer": 10, # Number of transfer, or number of passage
    "dilution": 1/1000, # Dilution factor for transfer
}

# Make initial state
N0 = make_synthetic_community(species_list = [13, 16, 18, 21, 51, 52, 74, 94, 115, 138, 142, 152, 155, 158, 171, 177, 180, 183, 200], assumptions = assumptions, number_species = 2, initial_frequency = [[0.5, 0.5], [0.95, 0.05], [0.05, 0.95]])
init_state = make_initial_state(assumptions, N0)

# Make plate
plate = Community(init_state, dynamics, params, scale = 10**6, parallel = True)

# Simulation
simulate_community(plate, assumptions, params_simulation, file_name = "data/self_assembly-pair-W14", write_composition = True)

Run W14 for longer

# Assumptions
assumptions = a_default.copy() # Start with default parameters
assumptions.update({'n_wells':96, 'c1' :0.01, 'muc':0.1, 'm':0}) # Switch off mortality

# Prepare experiment
params, species_pool = prepare_experiment(assumptions)

## Simulation parameters
params_simulation = {
    "n_propagation": 12, # Length of propagation, or hours within a growth cycle
    "n_transfer": 30, # Number of transfer, or number of passage
    "dilution": 1/1000, # Dilution factor for transfer
}

# Make initial state
N0 = make_synthetic_community(species_list = [13, 16, 18, 21, 51, 52, 74, 94, 115, 138, 142, 152, 155, 158, 171, 177, 180, 183, 200], assumptions = assumptions, number_species = 2, initial_frequency = [[0.5, 0.5], [0.95, 0.05], [0.05, 0.95]])
init_state = make_initial_state(assumptions, N0)

# Make plate
plate = Community(init_state, dynamics, params, scale = 10**6, parallel = True)

# Simulation
simulate_community(plate, assumptions, params_simulation, file_name = "data/self_assembly-pair-W14a", write_composition = True)

Plot result

if(write_all_csv) fwrite(df_pair_interaction, root$find_file("output/report/data/temp/df_pair_interaction.txt"))

List of grower identified by monoculture

df_mono %>%
  filter(Type == "consumer", Transfer == 10) %>%
  pull(ID) %>% as.character() %>% paste0(collapse = ", ")

[1] "13, 16, 17, 18, 21, 33, 38, 49, 51, 66, 68, 74, 83, 85, 94, 110, 115, 118, 142, 152, 155, 158, 171, 177, 183, 188, 191"

Pairwise competition in self-assembled communities

p_pair

[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

pdf(root$find_file("output/report/figure/04A-self_assembly-W14_pairwise_competition.pdf")); p_pair[[3]]; invisible(dev.off())

pdf(root$find_file("output/report/figure/04A-self_assembly-W14a_pairwise_competition.pdf")); p_pair[[4]]; invisible(dev.off())

Number of interaction types

p_interaction_frequency

pdf(root$find_file("output/report/figure/04A-self_assembly-pairwise_interactions.pdf"), width = 8, height = 5); p_interaction_frequency; invisible(dev.off())